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Franz L, Viljoen M, Askew S, Brown M, Dawson G, Di Martino JM, Sapiro G, Sebolai K, Seris N, Shabalala N, Stahmer A, Turner EL, de Vries PJ. Autism Caregiver Coaching in Africa (ACACIA): Protocol for a type 1-hybrid effectiveness-implementation trial. PLoS One 2024; 19:e0291883. [PMID: 38215154 PMCID: PMC10786379 DOI: 10.1371/journal.pone.0291883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 09/28/2023] [Indexed: 01/14/2024] Open
Abstract
BACKGROUND While early autism intervention can significantly improve outcomes, gaps in implementation exist globally. These gaps are clearest in Africa, where forty percent of the world's children will live by 2050. Task-sharing early intervention to non-specialists is a key implementation strategy, given the lack of specialists in Africa. Naturalistic Developmental Behavioral Interventions (NDBI) are a class of early autism intervention that can be delivered by caregivers. As a foundational step to address the early autism intervention gap, we adapted a non-specialist delivered caregiver coaching NDBI for the South African context, and pre-piloted this cascaded task-sharing approach in an existing system of care. OBJECTIVES First, we will test the effectiveness of the caregiver coaching NDBI compared to usual care. Second, we will describe coaching implementation factors within the Western Cape Department of Education in South Africa. METHODS This is a type 1 effectiveness-implementation hybrid design; assessor-blinded, group randomized controlled trial. Participants include 150 autistic children (18-72 months) and their caregivers who live in Cape Town, South Africa, and those involved in intervention implementation. Early Childhood Development practitioners, employed by the Department of Education, will deliver 12, one hour, coaching sessions to the intervention group. The control group will receive usual care. Distal co-primary outcomes include the Communication Domain Standard Score (Vineland Adaptive Behavior Scales, Third Edition) and the Language and Communication Developmental Quotient (Griffiths Scales of Child Development, Third Edition). Proximal secondary outcome include caregiver strategies measured by the sum of five items from the Joint Engagement Rating Inventory. We will describe key implementation determinants. RESULTS Participant enrolment started in April 2023. Estimated primary completion date is March 2027. CONCLUSION The ACACIA trial will determine whether a cascaded task-sharing intervention delivered in an educational setting leads to meaningful improvements in communication abilities of autistic children, and identify implementation barriers and facilitators. TRIAL REGISTRATION NCT05551728 in Clinical Trial Registry (https://clinicaltrials.gov).
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Affiliation(s)
- Lauren Franz
- Duke Center for Autism and Brain Development, Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina, United States of America
- Duke Global Health Institute, Duke University, Durham, North Carolina, United States of America
- Centre for Autism Research in Africa (CARA), Division of Child & Adolescent Psychiatry, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa
| | - Marisa Viljoen
- Centre for Autism Research in Africa (CARA), Division of Child & Adolescent Psychiatry, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa
| | - Sandy Askew
- Duke Global Health Institute, Duke University, Durham, North Carolina, United States of America
| | - Musaddiqah Brown
- Centre for Autism Research in Africa (CARA), Division of Child & Adolescent Psychiatry, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa
| | - Geraldine Dawson
- Duke Center for Autism and Brain Development, Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina, United States of America
| | - J Matias Di Martino
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, United States of America
| | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, United States of America
| | - Katlego Sebolai
- Centre for Autism Research in Africa (CARA), Division of Child & Adolescent Psychiatry, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa
| | - Noleen Seris
- Centre for Autism Research in Africa (CARA), Division of Child & Adolescent Psychiatry, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa
| | - Nokuthula Shabalala
- Centre for Autism Research in Africa (CARA), Division of Child & Adolescent Psychiatry, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa
| | - Aubyn Stahmer
- Center for Excellence in Developmental Disabilities, MIND Institute, University of California, Davis, Davis, California, United States of America
| | - Elizabeth L Turner
- Duke Global Health Institute, Duke University, Durham, North Carolina, United States of America
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, United States of America
| | - Petrus J de Vries
- Centre for Autism Research in Africa (CARA), Division of Child & Adolescent Psychiatry, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa
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Loftness BC, Halvorson-Phelan J, OLeary A, Bradshaw C, Prytherch S, Berman I, Torous J, Copeland WL, Cheney N, McGinnis RS, McGinnis EW. The ChAMP App: A Scalable mHealth Technology for Detecting Digital Phenotypes of Early Childhood Mental Health. IEEE J Biomed Health Inform 2023; PP:10.1109/JBHI.2023.3337649. [PMID: 38019617 PMCID: PMC11133764 DOI: 10.1109/jbhi.2023.3337649] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2023]
Abstract
Childhood mental health problems are common, impairing, and can become chronic if left untreated. Children are not reliable reporters of their emotional and behavioral health, and caregivers often unintentionally under- or over-report child symptoms, making assessment challenging. Objective physiological and behavioral measures of emotional and behavioral health are emerging. However, these methods typically require specialized equipment and expertise in data and sensor engineering to administer and analyze. To address this challenge, we have developed the ChAMP (Childhood Assessment and Management of digital Phenotypes) System, which includes a mobile application for collecting movement and audio data during a battery of mood induction tasks and an open-source platform for extracting digital biomarkers. As proof of principle, we present ChAMP System data from 101 children 4-8 years old, with and without diagnosed mental health disorders. Machine learning models trained on these data detect the presence of specific disorders with 70-73% balanced accuracy, with similar results to clinical thresholds on established parent-report measures (63-82% balanced accuracy). Features favored in model architectures are described using Shapley Additive Explanations (SHAP). Canonical Correlation Analysis reveals moderate to strong associations between predictors of each disorder and associated symptom severity (r = .51-.83). The open-source ChAMP System provides clinically-relevant digital biomarkers that may later complement parent-report measures of emotional and behavioral health for detecting kids with underlying mental health conditions and lowers the barrier to entry for researchers interested in exploring digital phenotyping of childhood mental health.
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Loftness BC, Halvorson-Phelan J, O'Leary A, Bradshaw C, Prytherch S, Berman I, Torous J, Copeland WL, Cheney N, McGinnis RS, McGinnis EW. The ChAMP App: A Scalable mHealth Technology for Detecting Digital Phenotypes of Early Childhood Mental Health. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.19.23284753. [PMID: 38076802 PMCID: PMC10705626 DOI: 10.1101/2023.01.19.23284753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Childhood mental health problems are common, impairing, and can become chronic if left untreated. Children are not reliable reporters of their emotional and behavioral health, and caregivers often unintentionally under- or over-report child symptoms, making assessment challenging. Objective physiological and behavioral measures of emotional and behavioral health are emerging. However, these methods typically require specialized equipment and expertise in data and sensor engineering to administer and analyze. To address this challenge, we have developed the ChAMP (Childhood Assessment and Management of digital Phenotypes) System, which includes a mobile application for collecting movement and audio data during a battery of mood induction tasks and an open-source platform for extracting digital biomarkers. As proof of principle, we present ChAMP System data from 101 children 4-8 years old, with and without diagnosed mental health disorders. Machine learning models trained on these data detect the presence of specific disorders with 70-73% balanced accuracy, with similar results to clinical thresholds on established parent-report measures (63-82% balanced accuracy). Features favored in model architectures are described using Shapley Additive Explanations (SHAP). Canonical Correlation Analysis reveals moderate to strong associations between predictors of each disorder and associated symptom severity (r = .51-.83). The open-source ChAMP System provides clinically-relevant digital biomarkers that may later complement parent-report measures of emotional and behavioral health for detecting kids with underlying mental health conditions and lowers the barrier to entry for researchers interested in exploring digital phenotyping of childhood mental health.
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Affiliation(s)
- Bryn C Loftness
- University of Vermont's Complex Systems Center and M-Sense Research Group
| | | | | | - Carter Bradshaw
- University of Vermont Medical Center Department of Psychiatry
| | | | - Isabel Berman
- University of Vermont Medical Center Department of Psychiatry
| | - John Torous
- Digital Psychiatry Division for Beth Israel Deaconess Medical Center at Harvard Medical School
| | | | - Nick Cheney
- University of Vermont Complex Systems Center
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Perochon S, Di Martino JM, Carpenter KLH, Compton S, Davis N, Eichner B, Espinosa S, Franz L, Krishnappa Babu PR, Sapiro G, Dawson G. Early detection of autism using digital behavioral phenotyping. Nat Med 2023; 29:2489-2497. [PMID: 37783967 PMCID: PMC10579093 DOI: 10.1038/s41591-023-02574-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 08/25/2023] [Indexed: 10/04/2023]
Abstract
Early detection of autism, a neurodevelopmental condition associated with challenges in social communication, ensures timely access to intervention. Autism screening questionnaires have been shown to have lower accuracy when used in real-world settings, such as primary care, as compared to research studies, particularly for children of color and girls. Here we report findings from a multiclinic, prospective study assessing the accuracy of an autism screening digital application (app) administered during a pediatric well-child visit to 475 (17-36 months old) children (269 boys and 206 girls), of which 49 were diagnosed with autism and 98 were diagnosed with developmental delay without autism. The app displayed stimuli that elicited behavioral signs of autism, quantified using computer vision and machine learning. An algorithm combining multiple digital phenotypes showed high diagnostic accuracy with the area under the receiver operating characteristic curve = 0.90, sensitivity = 87.8%, specificity = 80.8%, negative predictive value = 97.8% and positive predictive value = 40.6%. The algorithm had similar sensitivity performance across subgroups as defined by sex, race and ethnicity. These results demonstrate the potential for digital phenotyping to provide an objective, scalable approach to autism screening in real-world settings. Moreover, combining results from digital phenotyping and caregiver questionnaires may increase autism screening accuracy and help reduce disparities in access to diagnosis and intervention.
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Affiliation(s)
- Sam Perochon
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
- Ecole Normale Supérieure Paris-Saclay, Gif-sur-Yvette, France
| | - J Matias Di Martino
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Kimberly L H Carpenter
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Scott Compton
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Naomi Davis
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Brian Eichner
- Department of Pediatrics, Duke University, Durham, NC, USA
| | - Steven Espinosa
- Office of Information Technology, Duke University, Durham, NC, USA
| | - Lauren Franz
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Duke Global Health Institute, Duke University, Durham, NC, USA
| | | | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
- Departments of Biomedical Engineering, Mathematics, and Computer Science, Duke University, Durham, NC, USA
| | - Geraldine Dawson
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA.
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Franz L, Viljoen M, Askew S, Brown M, Dawson G, Di Martino JM, Sapiro G, Sebolai K, Seris N, Shabalala N, Stahmer A, Turner EL, de Vries PJ. Autism Caregiver Coaching in Africa (ACACIA): Protocol for a type 1-hybrid effectiveness-implementation trial. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.10.23295331. [PMID: 37745535 PMCID: PMC10516098 DOI: 10.1101/2023.09.10.23295331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Background While early autism intervention can significantly improve outcomes, gaps in implementation exist globally. These gaps are clearest in Africa, where forty percent of the world's children will live by 2050. Task-sharing early intervention to non-specialists is a key implementation strategy, given the lack of specialists in Africa. Naturalistic Developmental Behavioral Interventions (NDBI) are a class of early autism intervention that can be delivered by caregivers. As a foundational step to address the early autism intervention gap, we adapted a non-specialist delivered caregiver coaching NDBI for the South African context, and pre-piloted this cascaded task-sharing approach in an existing system of care. Objectives First, we will test the effectiveness of the caregiver coaching NDBI compared to usual care. Second, we will describe coaching implementation factors within the Western Cape Department of Education in South Africa. Methods This is a type 1 effectiveness-implementation hybrid design; assessor-blinded, group randomized controlled trial. Participants include 150 autistic children (18-72 months) and their caregivers who live in Cape Town, South Africa, and those involved in intervention implementation. Early Childhood Development practitioners, employed by the Department of Education, will deliver 12, one hour, coaching sessions to the intervention group. The control group will receive usual care. Distal co-primary outcomes include the Communication Domain Standard Score (Vineland Adaptive Behavior Scales, Third Edition) and the Language and Communication Developmental Quotient (Griffiths Scales of Child Development, Third Edition). Proximal secondary outcome include caregiver strategies measured by the sum of five items from the Joint Engagement Rating Inventory. We will describe key implementation determinants. Results Participant enrolment started in April 2023. Estimated primary completion date is March 2027. Conclusion The ACACIA trial will determine whether a cascaded task-sharing intervention delivered in an educational setting leads to meaningful improvements in communication abilities of autistic children, and identify implementation barriers and facilitators.
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Affiliation(s)
- Lauren Franz
- Duke Center for Autism and Brain Development, Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina, USA
- Duke Global Health Institute, Duke University, Durham, North Carolina, USA
- Centre for Autism Research in Africa (CARA), Division of Child & Adolescent Psychiatry, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa
| | - Marisa Viljoen
- Centre for Autism Research in Africa (CARA), Division of Child & Adolescent Psychiatry, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa
| | - Sandy Askew
- Duke Global Health Institute, Duke University, Durham, North Carolina, USA
| | - Musaddiqah Brown
- Centre for Autism Research in Africa (CARA), Division of Child & Adolescent Psychiatry, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa
| | - Geraldine Dawson
- Duke Center for Autism and Brain Development, Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina, USA
| | - J Matias Di Martino
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, USA
| | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, USA
| | - Katlego Sebolai
- Centre for Autism Research in Africa (CARA), Division of Child & Adolescent Psychiatry, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa
| | - Noleen Seris
- Centre for Autism Research in Africa (CARA), Division of Child & Adolescent Psychiatry, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa
| | - Nokuthula Shabalala
- Centre for Autism Research in Africa (CARA), Division of Child & Adolescent Psychiatry, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa
| | - Aubyn Stahmer
- Center for Excellence in Developmental Disabilities, MIND Institute, University of California Davis, California, USA
| | - Elizabeth L Turner
- Duke Global Health Institute, Duke University, Durham, North Carolina, USA
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA
| | - Petrus J de Vries
- Centre for Autism Research in Africa (CARA), Division of Child & Adolescent Psychiatry, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa
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O'Hara PT, Talero Cabrejo P, Earland TV. Early detection of neurodevelopmental disorders in paediatric primary care: A scoping review. Fam Pract 2023:cmad072. [PMID: 37491000 DOI: 10.1093/fampra/cmad072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/27/2023] Open
Abstract
BACKGROUND Earlier detection of children at risk for neurodevelopmental disorders is critical and has longstanding repercussions if not addressed early enough. OBJECTIVES To explore the supporting or facilitating characteristics of paediatric primary care models of care for early detection in infants and toddlers at risk for neurodevelopmental disorders, identify practitioners involved, and describe how they align with occupational therapy's scope of practice. METHODS A scoping review following the Joanna Briggs Institute framework was used. PubMed Central, Cumulative Index to Nursing & Allied Health Literature, and Scopus databases were searched. The search was conducted between January and February 2022. Inclusion criteria were: children aged 0-3 years old; neurodevelopmental disorders including cerebral palsy (CP) and autism spectrum disorder (ASD); models of care used in the paediatric primary care setting and addressing concepts of timing and plasticity; peer-reviewed literature written in English; published between 2010 and 2022. Study protocol registered at https://doi.org/10.17605/OSF.IO/MD4K5. RESULTS We identified 1,434 publications, yielding 22 studies that met inclusion criteria. Models of care characteristics included the use of technology, education to parents and staff, funding to utilize innovative models of care, assessment variability, organizational management changes, increased visit length, earlier timeline for neurodevelopmental screening, and collaboration with current office staff or nonphysician practitioners. The top 4 providers were paediatricians, general or family practitioners, nurse/nurse practitioners, and office staff. All studies aligned with occupational therapy health promotion scope of practice and intervention approach yet did not include occupational therapy within the paediatric primary care setting. CONCLUSIONS No studies included occupational therapy as a healthcare provider that could be used within the paediatric primary care setting. However, all studies demonstrated models of care facilitating characteristics aligning with occupational therapy practice. Models of care facilitating characteristics identified interdisciplinary staff as a major contributor, which can include occupational therapy, to improve early detection within paediatric primary care.
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Affiliation(s)
- Paulette T O'Hara
- Department of Public Health, California Children's Services, Los Angeles, CA, United States
- Department of Occupational Therapy, Thomas Jefferson University, Philadelphia, PA, United States
| | - Pamela Talero Cabrejo
- Department of Occupational Therapy, Thomas Jefferson University, Philadelphia, PA, United States
| | - Tracey V Earland
- Department of Occupational Therapy, Thomas Jefferson University, Philadelphia, PA, United States
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Coffman M, Di Martino JM, Aiello R, Carpenter KL, Chang Z, Compton S, Eichner B, Espinosa S, Flowers J, Franz L, Perochon S, Krishnappa Babu PR, Sapiro G, Dawson G. Relationship between quantitative digital behavioral features and clinical profiles in young autistic children. Autism Res 2023; 16:1360-1374. [PMID: 37259909 PMCID: PMC10524806 DOI: 10.1002/aur.2955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 05/06/2023] [Indexed: 06/02/2023]
Abstract
Early behavioral markers for autism include differences in social attention and orienting in response to one's name when called, and differences in body movements and motor abilities. More efficient, scalable, objective, and reliable measures of these behaviors could improve early screening for autism. This study evaluated whether objective and quantitative measures of autism-related behaviors elicited from an app (SenseToKnow) administered on a smartphone or tablet and measured via computer vision analysis (CVA) are correlated with standardized caregiver-report and clinician administered measures of autism-related behaviors and cognitive, language, and motor abilities. This is an essential step in establishing the concurrent validity of a digital phenotyping approach. In a sample of 485 toddlers, 43 of whom were diagnosed with autism, we found that CVA-based gaze variables related to social attention were associated with the level of autism-related behaviors. Two language-related behaviors measured via the app, attention to people during a conversation and responding to one's name being called, were associated with children's language skills. Finally, performance during a bubble popping game was associated with fine motor skills. These findings provide initial support for the concurrent validity of the SenseToKnow app and its potential utility in identifying clinical profiles associated with autism. Future research is needed to determine whether the app can be used as an autism screening tool, can reliably stratify autism-related behaviors, and measure changes in autism-related behaviors over time.
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Affiliation(s)
- Marika Coffman
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Department of Psychiatric and Behavioral Sciences, Duke University, Durham, NC, USA
| | - J. Matias Di Martino
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Rachel Aiello
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Department of Psychiatric and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Kimberly L.H. Carpenter
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Department of Psychiatric and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Zhuoqing Chang
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Scott Compton
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Department of Psychiatric and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Brian Eichner
- Department of Pediatrics, Duke University, Durham, NC, USA
| | - Steve Espinosa
- Office of Information Technology, Duke University, Durham, NC, USA
| | - Jacqueline Flowers
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Department of Psychiatric and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Lauren Franz
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Department of Psychiatric and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Sam Perochon
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
- Ecole Normale Superieure Paris-Saclay, Gif-Sur-Yvette, France
| | | | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
- Department of Biomedical Engineering, Mathematics, and Computer Sciences, Duke University, Durham, NC, USA
| | - Geraldine Dawson
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Department of Psychiatric and Behavioral Sciences, Duke University, Durham, NC, USA
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Krishnappa Babu PR, Aikat V, Di Martino JM, Chang Z, Perochon S, Espinosa S, Aiello R, L H Carpenter K, Compton S, Davis N, Eichner B, Flowers J, Franz L, Dawson G, Sapiro G. Blink rate and facial orientation reveal distinctive patterns of attentional engagement in autistic toddlers: a digital phenotyping approach. Sci Rep 2023; 13:7158. [PMID: 37137954 PMCID: PMC10156751 DOI: 10.1038/s41598-023-34293-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 04/27/2023] [Indexed: 05/05/2023] Open
Abstract
Differences in social attention are well-documented in autistic individuals, representing one of the earliest signs of autism. Spontaneous blink rate has been used to index attentional engagement, with lower blink rates reflecting increased engagement. We evaluated novel methods using computer vision analysis (CVA) for automatically quantifying patterns of attentional engagement in young autistic children, based on facial orientation and blink rate, which were captured via mobile devices. Participants were 474 children (17-36 months old), 43 of whom were diagnosed with autism. Movies containing social or nonsocial content were presented via an iPad app, and simultaneously, the device's camera recorded the children's behavior while they watched the movies. CVA was used to extract the duration of time the child oriented towards the screen and their blink rate as indices of attentional engagement. Overall, autistic children spent less time facing the screen and had a higher mean blink rate compared to neurotypical children. Neurotypical children faced the screen more often and blinked at a lower rate during the social movies compared to the nonsocial movies. In contrast, autistic children faced the screen less often during social movies than during nonsocial movies and showed no differential blink rate to social versus nonsocial movies.
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Affiliation(s)
| | - Vikram Aikat
- Department of Computer Science, Duke University, Durham, NC, USA
| | - J Matias Di Martino
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Zhuoqing Chang
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Sam Perochon
- Ecole Normale Supérieure Paris-Saclay, Gif-Sur-Yvette, France
| | - Steven Espinosa
- Office of Information Technology, Duke University, Durham, NC, USA
| | - Rachel Aiello
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Kimberly L H Carpenter
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Scott Compton
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Naomi Davis
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Brian Eichner
- Department of Pediatrics, Duke University, Durham, NC, USA
| | - Jacqueline Flowers
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Lauren Franz
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Geraldine Dawson
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA.
| | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA.
- Departments of Biomedical Engineering, Mathematics, and Computer Science, Duke University, Durham, NC, USA.
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Babu PRK, Di Martino JM, Chang Z, Perochon S, Carpenter KLH, Compton S, Espinosa S, Dawson G, Sapiro G. Exploring Complexity of Facial Dynamics in Autism Spectrum Disorder. IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 2023; 14:919-930. [PMID: 37266390 PMCID: PMC10231874 DOI: 10.1109/taffc.2021.3113876] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Atypical facial expression is one of the early symptoms of autism spectrum disorder (ASD) characterized by reduced regularity and lack of coordination of facial movements. Automatic quantification of these behaviors can offer novel biomarkers for screening, diagnosis, and treatment monitoring of ASD. In this work, 40 toddlers with ASD and 396 typically developing toddlers were shown developmentally-appropriate and engaging movies presented on a smart tablet during a well-child pediatric visit. The movies consisted of social and non-social dynamic scenes designed to evoke certain behavioral and affective responses. The front-facing camera of the tablet was used to capture the toddlers' face. Facial landmarks' dynamics were then automatically computed using computer vision algorithms. Subsequently, the complexity of the landmarks' dynamics was estimated for the eyebrows and mouth regions using multiscale entropy. Compared to typically developing toddlers, toddlers with ASD showed higher complexity (i.e., less predictability) in these landmarks' dynamics. This complexity in facial dynamics contained novel information not captured by traditional facial affect analyses. These results suggest that computer vision analysis of facial landmark movements is a promising approach for detecting and quantifying early behavioral symptoms associated with ASD.
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Affiliation(s)
| | - J Matias Di Martino
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Zhuoqing Chang
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Sam Perochon
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Kimberly L H Carpenter
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Scott Compton
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Steven Espinosa
- Office of Information Technology, Duke University, Durham, NC, USA
| | - Geraldine Dawson
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC. USA
| | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Biomedical Engineering, Mathematics, and Computer Sciences, Duke University, Durham, NC, USA
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10
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Dawson G, Rieder AD, Johnson MH. Prediction of autism in infants: progress and challenges. Lancet Neurol 2023; 22:244-254. [PMID: 36427512 PMCID: PMC10100853 DOI: 10.1016/s1474-4422(22)00407-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 09/17/2022] [Accepted: 09/27/2022] [Indexed: 11/24/2022]
Abstract
Autism spectrum disorder (henceforth autism) is a neurodevelopmental condition that can be reliably diagnosed in children by age 18-24 months. Prospective longitudinal studies of infants aged 1 year and younger who are later diagnosed with autism are elucidating the early developmental course of autism and identifying ways of predicting autism before diagnosis is possible. Studies that use MRI, EEG, and near-infrared spectroscopy have identified differences in brain development in infants later diagnosed with autism compared with infants without autism. Retrospective studies of infants younger than 1 year who received a later diagnosis of autism have also showed an increased prevalence of health conditions, such as sleep disorders, gastrointestinal disorders, and vision problems. Behavioural features of infants later diagnosed with autism include differences in attention, vocalisations, gestures, affect, temperament, social engagement, sensory processing, and motor abilities. Although research findings offer insight on promising screening approaches for predicting autism in infants, individual-level predictions remain a future goal. Multiple scientific challenges and ethical questions remain to be addressed to translate research on early brain-based and behavioural predictors of autism into feasible and reliable screening tools for clinical practice.
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Affiliation(s)
- Geraldine Dawson
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA.
| | - Amber D Rieder
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA; Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Mark H Johnson
- Department of Psychology, University of Cambridge, Cambridge, UK; Centre for Brain and Cognitive Development, Birkbeck, University of London, London, UK
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11
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Perochon S, Matias Di Martino J, Carpenter KLH, Compton S, Davis N, Espinosa S, Franz L, Rieder AD, Sullivan C, Sapiro G, Dawson G. A tablet-based game for the assessment of visual motor skills in autistic children. NPJ Digit Med 2023; 6:17. [PMID: 36737475 PMCID: PMC9898502 DOI: 10.1038/s41746-023-00762-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 01/21/2023] [Indexed: 02/05/2023] Open
Abstract
Increasing evidence suggests that early motor impairments are a common feature of autism. Thus, scalable, quantitative methods for measuring motor behavior in young autistic children are needed. This work presents an engaging and scalable assessment of visual-motor abilities based on a bubble-popping game administered on a tablet. Participants are 233 children ranging from 1.5 to 10 years of age (147 neurotypical children and 86 children diagnosed with autism spectrum disorder [autistic], of which 32 are also diagnosed with co-occurring attention-deficit/hyperactivity disorder [autistic+ADHD]). Computer vision analyses are used to extract several game-based touch features, which are compared across autistic, autistic+ADHD, and neurotypical participants. Results show that younger (1.5-3 years) autistic children pop the bubbles at a lower rate, and their ability to touch the bubble's center is less accurate compared to neurotypical children. When they pop a bubble, their finger lingers for a longer period, and they show more variability in their performance. In older children (3-10-years), consistent with previous research, the presence of co-occurring ADHD is associated with greater motor impairment, reflected in lower accuracy and more variable performance. Several motor features are correlated with standardized assessments of fine motor and cognitive abilities, as evaluated by an independent clinical assessment. These results highlight the potential of touch-based games as an efficient and scalable approach for assessing children's visual-motor skills, which can be part of a broader screening tool for identifying early signs associated with autism.
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Affiliation(s)
- Sam Perochon
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
- Ecole Normale Supérieure Paris-Saclay, Gif-Sur-Yvette, France
| | - J Matias Di Martino
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Kimberly L H Carpenter
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Scott Compton
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Naomi Davis
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Steven Espinosa
- Office of Information Technology, Duke University, Durham, NC, USA
| | - Lauren Franz
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Amber D Rieder
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Connor Sullivan
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA.
| | - Geraldine Dawson
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA.
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12
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Previously Marzena Szkodo MOR, Micai M, Caruso A, Fulceri F, Fazio M, Scattoni ML. Technologies to support the diagnosis and/or treatment of neurodevelopmental disorders: A systematic review. Neurosci Biobehav Rev 2023; 145:105021. [PMID: 36581169 DOI: 10.1016/j.neubiorev.2022.105021] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 12/13/2022] [Accepted: 12/23/2022] [Indexed: 12/27/2022]
Abstract
In recent years, there has been a great interest in utilizing technology in mental health research. The rapid technological development has encouraged researchers to apply technology as a part of a diagnostic process or treatment of Neurodevelopmental Disorders (NDDs). With the large number of studies being published comes an urgent need to inform clinicians and researchers about the latest advances in this field. Here, we methodically explore and summarize findings from studies published between August 2019 and February 2022. A search strategy led to the identification of 4108 records from PubMed and APA PsycInfo databases. 221 quantitative studies were included, covering a wide range of technologies used for diagnosis and/or treatment of NDDs, with the biggest focus on Autism Spectrum Disorder (ASD). The most popular technologies included machine learning, functional magnetic resonance imaging, electroencephalogram, magnetic resonance imaging, and neurofeedback. The results of the review indicate that technology-based diagnosis and intervention for NDD population is promising. However, given a high risk of bias of many studies, more high-quality research is needed.
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Affiliation(s)
| | - Martina Micai
- Research Coordination and Support Service, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy.
| | - Angela Caruso
- Research Coordination and Support Service, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy.
| | - Francesca Fulceri
- Research Coordination and Support Service, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy.
| | - Maria Fazio
- Department of Mathematics, Computer Science, Physics and Earth Sciences (MIFT), University of Messina, Viale F. Stagno d'Alcontres, 31, 98166 Messina, Italy.
| | - Maria Luisa Scattoni
- Research Coordination and Support Service, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy.
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13
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Babu PRK, Di Martino JM, Chang Z, Perochon S, Aiello R, Carpenter KL, Compton S, Davis N, Franz L, Espinosa S, Flowers J, Dawson G, Sapiro G. Complexity analysis of head movements in autistic toddlers. J Child Psychol Psychiatry 2023; 64:156-166. [PMID: 35965431 PMCID: PMC9771883 DOI: 10.1111/jcpp.13681] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/05/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND Early differences in sensorimotor functioning have been documented in young autistic children and infants who are later diagnosed with autism. Previous research has demonstrated that autistic toddlers exhibit more frequent head movement when viewing dynamic audiovisual stimuli, compared to neurotypical toddlers. To further explore this behavioral characteristic, in this study, computer vision (CV) analysis was used to measure several aspects of head movement dynamics of autistic and neurotypical toddlers while they watched a set of brief movies with social and nonsocial content presented on a tablet. METHODS Data were collected from 457 toddlers, 17-36 months old, during their well-child visit to four pediatric primary care clinics. Forty-one toddlers were subsequently diagnosed with autism. An application (app) displayed several brief movies on a tablet, and the toddlers watched these movies while sitting on their caregiver's lap. The front-facing camera in the tablet recorded the toddlers' behavioral responses. CV was used to measure the participants' head movement rate, movement acceleration, and complexity using multiscale entropy. RESULTS Autistic toddlers exhibited significantly higher rate, acceleration, and complexity in their head movements while watching the movies compared to neurotypical toddlers, regardless of the type of movie content (social vs. nonsocial). The combined features of head movement acceleration and complexity reliably distinguished the autistic and neurotypical toddlers. CONCLUSIONS Autistic toddlers exhibit differences in their head movement dynamics when viewing audiovisual stimuli. Higher complexity of their head movements suggests that their movements were less predictable and less stable compared to neurotypical toddlers. CV offers a scalable means of detecting subtle differences in head movement dynamics, which may be helpful in identifying early behaviors associated with autism and providing insight into the nature of sensorimotor differences associated with autism.
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Affiliation(s)
| | - J. Matias Di Martino
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Zhuoqing Chang
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Sam Perochon
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
- Ecole Normale Supérieure Paris-Saclay, Gif-Sur-Yvette, France
| | - Rachel Aiello
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Kimberly L.H. Carpenter
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Scott Compton
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Naomi Davis
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Lauren Franz
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Steven Espinosa
- Office of Information Technology, Duke University, Durham, NC, USA
| | - Jacqueline Flowers
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Geraldine Dawson
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
- Department of Biomedical Engineering, Mathematics, and Computer Sciences, Duke University, Durham, NC, USA
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14
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Zhu FL, Wang SH, Liu WB, Zhu HL, Li M, Zou XB. A multimodal machine learning system in early screening for toddlers with autism spectrum disorders based on the response to name. Front Psychiatry 2023; 14:1039293. [PMID: 36778637 PMCID: PMC9909188 DOI: 10.3389/fpsyt.2023.1039293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 01/05/2023] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Reduced or absence of the response to name (RTN) has been widely reported as an early specific indicator for autism spectrum disorder (ASD), while few studies have quantified the RTN of toddlers with ASD in an automatic way. The present study aims to apply a multimodal machine learning system (MMLS) in early screening for toddlers with ASD based on the RTN. METHODS A total of 125 toddlers were recruited, including ASD (n = 61), developmental delay (DD, n = 31), and typical developmental (TD, n = 33). Procedures of RTN were, respectively, performed by the evaluator and caregiver. Behavioral data were collected by eight-definition tripod-mounted cameras and coded by the MMLS. Response score, response time, and response duration time were accurately calculated to evaluate RTN. RESULTS Total accuracy of RTN scores rated by computers was 0.92. In both evaluator and caregiver procedures, toddlers with ASD had significant differences in response score, response time, and response duration time, compared to toddlers with DD and TD (all P-values < 0.05). The area under the curve (AUC) was 0.81 for the computer-rated results, and the AUC was 0.91 for the human-rated results. The accuracy in the identification of ASD based on the computer- and human-rated results was, respectively, 74.8 and 82.9%. There was a significant difference between the AUC of the human-rated results and computer-rated results (Z = 2.71, P-value = 0.007). CONCLUSION The multimodal machine learning system can accurately quantify behaviors in RTN procedures and may effectively distinguish toddlers with ASD from the non-ASD group. This novel system may provide a low-cost approach to early screening and identifying toddlers with ASD. However, machine learning is not as accurate as a human observer, and the detection of a single symptom like RTN is not sufficient enough to detect ASD.
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Affiliation(s)
- Feng-Lei Zhu
- Child Developmental and Behavioral Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Shi-Huan Wang
- Child Developmental and Behavioral Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Wen-Bo Liu
- School of Electronics and Information Technology, Guangzhou Higher Education Mega Center, Sun Yat-sen University, Guangzhou, China
| | - Hui-Lin Zhu
- Child Developmental and Behavioral Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Ming Li
- Data Science Research Center, Duke Kunshan University, Kunshan, China
| | - Xiao-Bing Zou
- Child Developmental and Behavioral Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
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15
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Potier R. Revue critique sur le potentiel du numérique dans la recherche en psychopathologie : un point de vue psychanalytique. L'ÉVOLUTION PSYCHIATRIQUE 2022. [DOI: 10.1016/j.evopsy.2022.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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16
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Meimei L, Zenghui M. A systematic review of telehealth screening, assessment, and diagnosis of autism spectrum disorder. Child Adolesc Psychiatry Ment Health 2022; 16:79. [PMID: 36209100 PMCID: PMC9547568 DOI: 10.1186/s13034-022-00514-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 09/21/2022] [Indexed: 11/18/2022] Open
Abstract
There is a significant delay between parents having concerns and receiving a formal assessment and Autism Spectrum Disorder (ASD) diagnosis. Telemedicine could be an effective alternative that shortens the waiting time for parents and primary health providers in ASD screening and diagnosis. We conducted a systematic review examining the uses of telemedicine technology for ASD screening, assessment, or diagnostic purposes and to what extent sample characteristics and psychometric properties were reported. This study searched four databases from 2000 to 2022 and obtained 26 studies that met the inclusion criteria. The 17 applications used in these 26 studies were divided into three categories based on their purpose: screening, diagnostic, and assessment. The results described the data extracted, including study characteristics, applied methods, indicators seen, and psychometric properties. Among the 15 applications with psychometric properties reported, the sensitivity ranged from 0.70 to 1, and the specificity ranged from 0.38 to 1. The present study highlights the strengths and weaknesses of current telemedicine approaches and provides a basis for future research. More rigorous empirical studies with larger sample sizes are needed to understand the feasibility, strengths, and limitations of telehealth technologies for screening, assessing, and diagnosing ASD.
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Affiliation(s)
- Liu Meimei
- grid.12380.380000 0004 1754 9227Vrije University Amsterdam, Amsterdam, The Netherlands
| | - Ma Zenghui
- Beijing ALSOABA Technology Co. LTD, ALSOLIFE, Beijing, China
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17
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Zampella CJ, Wang LAL, Haley M, Hutchinson AG, de Marchena A. Motor Skill Differences in Autism Spectrum Disorder: a Clinically Focused Review. Curr Psychiatry Rep 2021; 23:64. [PMID: 34387753 DOI: 10.1007/s11920-021-01280-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/12/2021] [Indexed: 12/26/2022]
Abstract
PURPOSE OF REVIEW This review synthesizes recent, clinically relevant findings on the scope, significance, and centrality of motor skill differences in autism spectrum disorder (ASD). RECENT FINDINGS Motor challenges in ASD are pervasive, clinically meaningful, and highly underrecognized, with up to 87% of the autistic population affected but only a small percentage receiving motor-focused clinical care. Across development, motor differences are associated with both core autism symptoms and broader functioning, though the precise nature of those associations and the specificity of motor profiles to ASD remain unestablished. Findings suggest that motor difficulties in ASD are quantifiable and treatable, and that detection and intervention efforts targeting motor function may also positively influence social communication. Recent evidence supports a need for explicit recognition of motor impairment within the diagnostic framework of ASD as a clinical specifier. Motor differences in ASD warrant greater clinical attention and routine incorporation into screening, evaluation, and treatment planning.
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Affiliation(s)
- Casey J Zampella
- Center for Autism Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
| | - Leah A L Wang
- Center for Autism Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Margaret Haley
- Center for Autism Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Anne G Hutchinson
- Center for Autism Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Ashley de Marchena
- Department of Behavioral and Social Sciences, University of the Sciences, Philadelphia, PA, USA
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18
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Chang Z, Di Martino JM, Aiello R, Baker J, Carpenter K, Compton S, Davis N, Eichner B, Espinosa S, Flowers J, Franz L, Harris A, Howard J, Perochon S, Perrin EM, Krishnappa Babu PR, Spanos M, Sullivan C, Walter BK, Kollins SH, Dawson G, Sapiro G. Computational Methods to Measure Patterns of Gaze in Toddlers With Autism Spectrum Disorder. JAMA Pediatr 2021; 175:827-836. [PMID: 33900383 PMCID: PMC8077044 DOI: 10.1001/jamapediatrics.2021.0530] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Accepted: 02/05/2021] [Indexed: 12/18/2022]
Abstract
Importance Atypical eye gaze is an early-emerging symptom of autism spectrum disorder (ASD) and holds promise for autism screening. Current eye-tracking methods are expensive and require special equipment and calibration. There is a need for scalable, feasible methods for measuring eye gaze. Objective Using computational methods based on computer vision analysis, we evaluated whether an app deployed on an iPhone or iPad that displayed strategically designed brief movies could elicit and quantify differences in eye-gaze patterns of toddlers with ASD vs typical development. Design, Setting, and Participants A prospective study in pediatric primary care clinics was conducted from December 2018 to March 2020, comparing toddlers with and without ASD. Caregivers of 1564 toddlers were invited to participate during a well-child visit. A total of 993 toddlers (63%) completed study measures. Enrollment criteria were aged 16 to 38 months, healthy, English- or Spanish-speaking caregiver, and toddler able to sit and view the app. Participants were screened with the Modified Checklist for Autism in Toddlers-Revised With Follow-up during routine care. Children were referred by their pediatrician for diagnostic evaluation based on results of the checklist or if the caregiver or pediatrician was concerned. Forty toddlers subsequently were diagnosed with ASD. Exposures A mobile app displayed on a smartphone or tablet. Main Outcomes and Measures Computer vision analysis quantified eye-gaze patterns elicited by the app, which were compared between toddlers with ASD vs typical development. Results Mean age of the sample was 21.1 months (range, 17.1-36.9 months), and 50.6% were boys, 59.8% White individuals, 16.5% Black individuals, 23.7% other race, and 16.9% Hispanic/Latino individuals. Distinctive eye-gaze patterns were detected in toddlers with ASD, characterized by reduced gaze to social stimuli and to salient social moments during the movies, and previously unknown deficits in coordination of gaze with speech sounds. The area under the receiver operating characteristic curve discriminating ASD vs non-ASD using multiple gaze features was 0.90 (95% CI, 0.82-0.97). Conclusions and Relevance The app reliably measured both known and new gaze biomarkers that distinguished toddlers with ASD vs typical development. These novel results may have potential for developing scalable autism screening tools, exportable to natural settings, and enabling data sets amenable to machine learning.
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Affiliation(s)
- Zhuoqing Chang
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina
| | - J. Matias Di Martino
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina
| | - Rachel Aiello
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina
- Duke Center for Autism and Brain Development, Duke University, Durham, North Carolina
| | - Jeffrey Baker
- Department of Pediatrics, Duke University, Durham, North Carolina
| | - Kimberly Carpenter
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina
- Duke Center for Autism and Brain Development, Duke University, Durham, North Carolina
| | - Scott Compton
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina
- Duke Center for Autism and Brain Development, Duke University, Durham, North Carolina
| | - Naomi Davis
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina
- Duke Center for Autism and Brain Development, Duke University, Durham, North Carolina
| | - Brian Eichner
- Department of Pediatrics, Duke University, Durham, North Carolina
| | - Steven Espinosa
- Office of Information Technology, Duke University, Durham, North Carolina
| | - Jacqueline Flowers
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina
- Duke Center for Autism and Brain Development, Duke University, Durham, North Carolina
| | - Lauren Franz
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina
- Duke Center for Autism and Brain Development, Duke University, Durham, North Carolina
- Duke Global Health Institute, Duke University, Durham, North Carolina
| | - Adrianne Harris
- Duke Center for Autism and Brain Development, Duke University, Durham, North Carolina
- Department of Psychology & Neuroscience, Duke University, Durham, North Carolina
| | - Jill Howard
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina
- Duke Center for Autism and Brain Development, Duke University, Durham, North Carolina
| | - Sam Perochon
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina
- Ecole Normale Supérieure Paris-Saclay, Cachan, France
| | - Eliana M. Perrin
- Department of Pediatrics, Duke University, Durham, North Carolina
- Duke Center for Childhood Obesity Research, Duke University, Durham, North Carolina
| | | | - Marina Spanos
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina
- Duke Center for Autism and Brain Development, Duke University, Durham, North Carolina
| | - Connor Sullivan
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina
- Duke Center for Autism and Brain Development, Duke University, Durham, North Carolina
| | | | - Scott H. Kollins
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina
- Duke Center for Autism and Brain Development, Duke University, Durham, North Carolina
| | - Geraldine Dawson
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina
- Duke Center for Autism and Brain Development, Duke University, Durham, North Carolina
- Duke Global Health Institute, Duke University, Durham, North Carolina
- Duke Institute for Brain Sciences, Duke University, Durham, North Carolina
| | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina
- Duke Center for Autism and Brain Development, Duke University, Durham, North Carolina
- Department of Biomedical Engineering, Mathematics, and Computer Sciences, Duke University, Durham, North Carolina
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19
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Zhao Z, Zhu Z, Zhang X, Tang H, Xing J, Hu X, Lu J, Qu X. Identifying Autism with Head Movement Features by Implementing Machine Learning Algorithms. J Autism Dev Disord 2021; 52:3038-3049. [PMID: 34250557 DOI: 10.1007/s10803-021-05179-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/28/2021] [Indexed: 11/27/2022]
Abstract
Our study investigated the feasibility of using head movement features to identify individuals with autism spectrum disorder (ASD). Children with ASD and typical development (TD) were required to answer ten yes-no questions, and they were encouraged to nod/shake head while doing so. The head rotation range (RR) and the amount of rotation per minute (ARPM) in the pitch (head nodding direction), yaw (head shaking direction) and roll (lateral head inclination) directions were computed, and further fed into machine learning classifiers as the input features. The maximum classification accuracy of 92.11% was achieved with the decision tree classifier with two features (i.e., RR_Pitch and ARPM_Yaw). Our study suggests that head movement dynamics contain objective biomarkers that could identify ASD.
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Affiliation(s)
- Zhong Zhao
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, 3688 Nanhai, Avenue, Shenzhen City, Guangdong Province, China
| | - Zhipeng Zhu
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, 3688 Nanhai, Avenue, Shenzhen City, Guangdong Province, China
| | - Xiaobin Zhang
- Shenzhen Guangming District Center for Disease Control and Prevention, Shenzhen, China
| | - Haiming Tang
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, 3688 Nanhai, Avenue, Shenzhen City, Guangdong Province, China
| | - Jiayi Xing
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, 3688 Nanhai, Avenue, Shenzhen City, Guangdong Province, China
| | - Xinyao Hu
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, 3688 Nanhai, Avenue, Shenzhen City, Guangdong Province, China
| | - Jianping Lu
- Department of Child Psychiatry of Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, China
| | - Xingda Qu
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, 3688 Nanhai, Avenue, Shenzhen City, Guangdong Province, China.
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